
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 			SCREAMING_SNAKE_CASE__      (     UpperCAmelCase__):







				def __init__(       self						,				A_						,				A_						,				A_						,				A_ = None						,				)->		Optional[int]:







											'''simple docstring'''






											super().__init__()
											self.register_modules(transformer=_a						,				vae=_a						,				scheduler=_a     )

											# create a imagenet -> id dictionary for easier use
											UpperCamelCase   			=     {}
											if idalabel is not None:
																		for key, value in idalabel.items():
																									for label in value.split(','     ):
																																UpperCamelCase   			=     int(_a     )
																		UpperCamelCase   			=     dict(sorted(self.labels.items()     )     )







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[str]:







											'''simple docstring'''






											if not isinstance(_a						,				_a     ):
																		UpperCamelCase   			=     list(_a     )

											for l in label:
																		if l not in self.labels:
																									raise ValueError(
																									    F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''     )

											return [self.labels[l] for l in label]







				@torch.no_grad()
				def __call__(       self						,				A_						,				A_ = 4.0						,				A_ = None						,				A_ = 50						,				A_ = "pil"						,				A_ = True						,				)->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     len(_a     )
											UpperCamelCase   			=     self.transformer.config.sample_size
											UpperCamelCase   			=     self.transformer.config.in_channels

											UpperCamelCase   			=     randn_tensor(
											    shape=(batch_size, latent_channels, latent_size, latent_size)						,				generator=_a						,				device=self.device						,				dtype=self.transformer.dtype						,				)
											UpperCamelCase   			=     torch.cat([latents] * 2     ) if guidance_scale > 1 else latents

											UpperCamelCase   			=     torch.tensor(_a						,				device=self.device     ).reshape(-1     )
											UpperCamelCase   			=     torch.tensor([1000] * batch_size						,				device=self.device     )
											UpperCamelCase   			=     torch.cat([class_labels, class_null]						,				0     ) if guidance_scale > 1 else class_labels

											# set step values
											self.scheduler.set_timesteps(_a     )

											for t in self.progress_bar(self.scheduler.timesteps     ):
																		if guidance_scale > 1:
																									UpperCamelCase   			=     latent_model_input[: len(_a     ) // 2]
																									UpperCamelCase   			=     torch.cat([half, half]						,				dim=0     )
																		UpperCamelCase   			=     self.scheduler.scale_model_input(_a						,				_a     )

																		UpperCamelCase   			=     t
																		if not torch.is_tensor(_a     ):
																									# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																									# This would be a good case for the `match` statement (Python 3.10+)
																									UpperCamelCase   			=     latent_model_input.device.type == 'mps'
																									if isinstance(_a						,				_a     ):
																																UpperCamelCase   			=     torch.floataa if is_mps else torch.floataa
																									else:
																																UpperCamelCase   			=     torch.intaa if is_mps else torch.intaa
																									UpperCamelCase   			=     torch.tensor([timesteps]						,				dtype=_a						,				device=latent_model_input.device     )
																		elif len(timesteps.shape     ) == 0:
																									UpperCamelCase   			=     timesteps[None].to(latent_model_input.device     )
																		# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																		UpperCamelCase   			=     timesteps.expand(latent_model_input.shape[0]     )
																		# predict noise model_output
																		UpperCamelCase   			=     self.transformer(
																		    _a						,				timestep=_a						,				class_labels=_a     ).sample

																		# perform guidance
																		if guidance_scale > 1:
																									UpperCamelCase			,	UpperCamelCase   			=     noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																									UpperCamelCase			,	UpperCamelCase   			=     torch.split(_a						,				len(_a     ) // 2						,				dim=0     )

																									UpperCamelCase   			=     uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																									UpperCamelCase   			=     torch.cat([half_eps, half_eps]						,				dim=0     )

																									UpperCamelCase   			=     torch.cat([eps, rest]						,				dim=1     )

																		# learned sigma
																		if self.transformer.config.out_channels // 2 == latent_channels:
																									UpperCamelCase			,	UpperCamelCase   			=     torch.split(_a						,				_a						,				dim=1     )
																		else:
																									UpperCamelCase   			=     noise_pred

																		# compute previous image: x_t -> x_t-1
																		UpperCamelCase   			=     self.scheduler.step(_a						,				_a						,				_a     ).prev_sample

											if guidance_scale > 1:
																		UpperCamelCase			,	UpperCamelCase   			=     latent_model_input.chunk(2						,				dim=0     )
											else:
																		UpperCamelCase   			=     latent_model_input

											UpperCamelCase   			=     1 / self.vae.config.scaling_factor * latents
											UpperCamelCase   			=     self.vae.decode(_a     ).sample

											UpperCamelCase   			=     (samples / 2 + 0.5).clamp(0						,				1     )

											# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
											UpperCamelCase   			=     samples.cpu().permute(0						,				2						,				3						,				1     ).float().numpy()

											if output_type == "pil":
																		UpperCamelCase   			=     self.numpy_to_pil(_a     )

											if not return_dict:
																		return (samples,)

											return ImagePipelineOutput(images=_a     )










"""simple docstring"""




from __future__ import annotations

from decimal import Decimal

from numpy import array



def        a__					(							snake_case__							)		->   list[list[float]]:
			lowerCamelCase      =   Decimal

			# Check if the provided matrix has 2 rows and 2 columns
			# since this implementation only works for 2x2 matrices
			if len(snake_case__							) == 2 and len(matrix[0]							) == 2 and len(matrix[1]							) == 2:
						# Calculate the determinant of the matrix
						lowerCamelCase      =   float(
						    d(matrix[0][0]							) * d(matrix[1][1]							) - d(matrix[1][0]							) * d(matrix[0][1]							)							)
						if determinant == 0:
									raise ValueError("""This matrix has no inverse."""							)

						# Creates a copy of the matrix with swapped positions of the elements
						lowerCamelCase      =   [[0.0, 0.0], [0.0, 0.0]]
						lowerCamelCase ,  lowerCamelCase      =   matrix[1][1], matrix[0][0]
						lowerCamelCase ,  lowerCamelCase      =   -matrix[1][0], -matrix[0][1]

						# Calculate the inverse of the matrix
						return [
						    [(float(d(snake_case__							)							) / determinant) or 0.0 for n in row] for row in swapped_matrix
						]
			elif (
			    len(snake_case__							) == 3
			    and len(matrix[0]							) == 3
			    and len(matrix[1]							) == 3
			    and len(matrix[2]							) == 3
			):
						# Calculate the determinant of the matrix using Sarrus rule
						lowerCamelCase      =   float(
						    (
						        (d(matrix[0][0]							) * d(matrix[1][1]							) * d(matrix[2][2]							))
						        + (d(matrix[0][1]							) * d(matrix[1][2]							) * d(matrix[2][0]							))
						        + (d(matrix[0][2]							) * d(matrix[1][0]							) * d(matrix[2][1]							))
						    )
						    - (
						        (d(matrix[0][2]							) * d(matrix[1][1]							) * d(matrix[2][0]							))
						        + (d(matrix[0][1]							) * d(matrix[1][0]							) * d(matrix[2][2]							))
						        + (d(matrix[0][0]							) * d(matrix[1][2]							) * d(matrix[2][1]							))
						    )							)
						if determinant == 0:
									raise ValueError("""This matrix has no inverse."""							)

						# Creating cofactor matrix
						lowerCamelCase      =   [
						    [d(0.0							), d(0.0							), d(0.0							)],
						    [d(0.0							), d(0.0							), d(0.0							)],
						    [d(0.0							), d(0.0							), d(0.0							)],
						]
						lowerCamelCase      =   (d(matrix[1][1]							) * d(matrix[2][2]							)) - (
						    d(matrix[1][2]							) * d(matrix[2][1]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[1][0]							) * d(matrix[2][2]							)) - (d(matrix[1][2]							) * d(matrix[2][0]							))
						)
						lowerCamelCase      =   (d(matrix[1][0]							) * d(matrix[2][1]							)) - (
						    d(matrix[1][1]							) * d(matrix[2][0]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[0][1]							) * d(matrix[2][2]							)) - (d(matrix[0][2]							) * d(matrix[2][1]							))
						)
						lowerCamelCase      =   (d(matrix[0][0]							) * d(matrix[2][2]							)) - (
						    d(matrix[0][2]							) * d(matrix[2][0]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[0][0]							) * d(matrix[2][1]							)) - (d(matrix[0][1]							) * d(matrix[2][0]							))
						)
						lowerCamelCase      =   (d(matrix[0][1]							) * d(matrix[1][2]							)) - (
						    d(matrix[0][2]							) * d(matrix[1][1]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[0][0]							) * d(matrix[1][2]							)) - (d(matrix[0][2]							) * d(matrix[1][0]							))
						)
						lowerCamelCase      =   (d(matrix[0][0]							) * d(matrix[1][1]							)) - (
						    d(matrix[0][1]							) * d(matrix[1][0]							)
						)

						# Transpose the cofactor matrix (Adjoint matrix)
						lowerCamelCase      =   array(snake_case__							)
						for i in range(3							):
									for j in range(3							):
												lowerCamelCase      =   cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
						lowerCamelCase      =   array(snake_case__							)
						for i in range(3							):
									for j in range(3							):
												inverse_matrix[i][j] /= d(snake_case__							)

        # Calculate the inverse of the matrix
						return [[float(d(snake_case__							)							) or 0.0 for n in row] for row in inverse_matrix]
			raise ValueError("""Please provide a matrix of size 2x2 or 3x3."""							)



